Work

Insurance ratemaking for climate-related claim counts using mixed Poisson spatio-temporal and temporal Besag regression models

Year: 2026

Type: article

Abstract: This paper introduces a unified framework of mixed Poisson spatio-temporal regression models for climate-related property insurance claims. Our approach integrates two model specifications which have been studied separately in the literature, a spatio-temporal (SP) model that explicitly accounts for spatial autocorrelation, and a temporal Besag model that leverages spatial random effects to smooth regional variations. For expository purposes, the spatio-temporal Negative Binomial (SP-NB) and temporal Besag Negative Binomial (Besag-NB) regression models are fitted to claim data related to flood and flood–windstorm events from a Greek property insurance company over the period 2012–2022. Parameter estimation is performed using an Expectation–Maximization algorithm for the SP-NB model and Integrated Nested Laplace Approximations for the Besag-NB model. Finally, the a posteriori (bonus–malus) premium rates derived from these models incorporate property-specific characteristics, geographical information, regional trends, individual experiences, and a flood vulnerability index that accurately reflects true exposure in flood-prone areas.

Source: European Actuarial Journal

DOI: 10.1007/s13385-025-00443-6

Authors: Ragnar L. Gudmundarson, George Tzougas, Dimitris Christopoulos

Institutions: Heriot-Watt University, Maxwell Institute for Mathematical Sciences, University of Vienna

Language: English

Topic: Probability and Risk Models

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $2,290